The station is located on the summit (plateau) of the second highest point of the Hoggar mountain range in the Saharan desert. The site is very remote at a distance of 50 km from Tamanrasset. Touristic activities in the area are limited due to difficult access to a few dozen visitors per day. Vegetation is extremely sparse.
The Assekrem station allows us to get a clean read with little local bias due to the remoteness of the location. This allows us to get atmospheric readings of the specific gases as opposed to local short-lived and erratic gas emissions. For example, CO2 travels and mixes in the atmosphere due to its long lifetime, allowing us to see this value as opposed to having a station right next to an industrial city, where readings will largely depend on the production for that specific factory. The high position of the station can indicate that the readings are safe from biases induced from the thermal inversion layer of the atmosphere, including breeze, wind changes, and reduced or enhanced molecular transport. This provides a sturdy ground for atmospheric readings.
The data is collected in the Assekrem station in Algeria. Located at 23.2625° North, 5.6322° East, and at a 2710 meter elevation, this is one of the most remote stations and one of the only ones in the entire Saharan region. Many different techniques are used to monitor gases on Earth, but all have some type of limitations. For this specific case, the station uses in-situ measurements, which involve a single location using high quality, wide set of instrumentation, which can take precise measurements of a small and specific geographical point. two main limitations arise from this method:
In situ measurements provide what is best described as direct observations of the system. The great advantage is the way we can make use of a diverse set of instrumentation which can take the most accurate readings we can get.
Prophet is an algorithm developed by Facebook’s data team in order to automate a routine for forecasting trends given equally spaced data points (Taylor et. al, 2017). The strategy is to frame the forecasting problem as a curve-fitting by disentangling two components:
The parameters of the model are adjustable (eg., adding seasonalities, specifying forecast frequency, etc) which offers us the advantages of the Bayesian approach since the forecasts are derived from the posterior distribution.
from fbprophet import *
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
def forecasting(gas_name, gas_code, months, unit):
gas_data = pd.read_table('C:/Users/Taha/Desktop/algeria_ghg/'\
+gas_code+'_ask_surface-flask_1_ccgg_month.txt',
sep='\s{1,}', names=['stations','year','month','y'], engine='python')
gas_data['ds'] = pd.to_datetime(gas_data[['year', 'month']].assign(DAY=1))
model = Prophet(weekly_seasonality=False, daily_seasonality=False)
model.set_auto_seasonalities
model.add_seasonality(name='monthly', period=30, fourier_order=5)
model.fit(gas_data)
future = model.make_future_dataframe(periods=months,freq='M')
forecast = model.predict(future)
py.init_notebook_mode()
fig1 = plot.plot_plotly(model, forecast)
fig1.update_layout(title=str(gas_name)+' Forecast', xaxis_title='Time',
yaxis_title='Parts per '+unit+'illion (PP'+unit+')')
py.iplot(fig1)
fig2 = plot.plot_components_plotly(model, forecast)
fig2.update_layout(title=str(gas_name)+'<br>Trend & Seasonality', height=500)
py.iplot(fig2)
$\textbf{1. Gas measurements oscillations}$
$CO_{2}$ data points show a distinct seasonality in the measurements. Most of the landmass on Earth lies on the northern hemisphere, and when we look closely at the data, we are able to see that the smaller downward trends coincide with northern hemisphere spring and summer and the upward trend coincides with autumn and winter. This is because of the natural carbon cycle, often referred to as Earth’s breathing “breathing”. Spring and summer bring increased plan life, allowing them to remove carbon from the atmosphere, acting as a strong sink for $CO_{2}$. On the other hand autumn and winter represent the demise of many of these seasonal plants removing the carbon sink, and releasing organic carbon found in plants to the atmosphere.
$\textbf{2. Sink and sources}$
Human activity is one of the primary drivers of $CO_{2}$ increase over time. $CO_{2}$ exists in the atmosphere in a balance between generation and removal of the gass, driven by different processes. Common sources of $CO_{2}$ include natural and anthropogenic activity, namely, respiration, decomposition, volcanism, industrial activity, and transportation. More specific to the station’s region, we see strong human activity stemming from transportation and the power sector.
Equally important, the sinks represent a way for the environment to deposit, remove, or disperse a specific chemical in the atmosphere. Some of the most important sinks of $CO_{2}$ include outward transport, chemical removal, ocean absorption, soil deposition, and plant respiration. Broadly speaking, the most active carbon pools on land are living biomass and soil organic carbon. Taking into consideration the region, we have to account for desert sinks, which are limited due to the lack of vegetation, however, desert basins also act as carbon sinks and store it underground. As explained in Li et al. (2015), dissolved inorganic carbon is leached from irrigated arid land and deposited in saline/alkaline aquifers found under the desert. Since this region has limited local sources and sinks of carbon, we can expect to regard transport as one of the main drivers of data patterns in the region having industrial activity and ocean absorption as some of the most significant balancing processes in action.
$\textbf{3. Important facts}$
$CO_{2}$ has a long lifetime, which is one of the biggest reasons why it is such a powerful greenhouse gas, and is present globally, as opposed to locally isolated over industrial areas. This long lifetime means that identifying specific sources for this station is quite complex, specifically since large urban areas are not particularly close to it.
Wind patterns are particularly important for high $CO_{2}$ levels in this region as they blow industrial activity into the desert from coastal cities in North Africa and even Europe. These distances are possible due to the long lifetime of $CO_{2}$ in the atmosphere.
$\textbf{4. Global Warming Potential (GWP)}$
The Global Warming Potential (GWP) indicator relates to the heat absorbed by a greenhouse gas in the atmosphere. GWP depends on the following factors:
Being the most prominent greenhouse gas in terms of quantity, $CO_{2}$ is set as the baseline for GWP with a given value of 1. This means that other gases are measured as multipliers if we were to have the same amount. This information is relevant to understanding the GWP of the other gases targeted in this report.
$\textbf{5. Current state}$
In 2018, the global average carbon dioxide concentration was 407.4 ppm. This is the highest value of $CO_{2}$ over the past 800,000 years. Since we are concerned with the anthropogenic impact on $CO_{2}$ concentrations, it's most relevant to look at stable pre-industrial carbon concentrations, which stand at around 280 ppm.
Given human activity, it is clear from data collected in this station that there is an upward trend that extends all the way since the start of the industrial revolution. In modern times, levels continue to accelerate with the rise of developing industrial economies. $CO_{2}$ is a naturally occurring and necessary gas for conserving the conditions needed for a warm habitable planet, however humans have changed the natural course drastically, threatening to accelerate natural processes such as sea level rise, melting ice caps, and climate change.
forecasting('Carbon Dioxide', 'co2', 240, 'M')
forecasting('Methane', 'ch4', 240, 'B')
forecasting('Carbon Monoxide', 'co', 240, 'B')